Modular organization of functional brain networks in patients with degenerative cervical myelopathy

Previous studies have indicated that brain functional plasticity and reorganization in patients with degenerative cervical myelopathy (DCM). However, the effects of cervical cord compression on the functional integration and separation between and/or within modules remain unclear. This study aimed to address these questions using graph theory. Functional MRI was conducted on 46 DCM patients and 35 healthy controls (HCs). The intra- and inter-modular connectivity properties of the whole-brain functional network and nodal topological properties were then calculated using theoretical graph analysis. The difference in categorical variables between groups was compared using a chi-squared test, while that between continuous variables was evaluated using a two-sample t-test. Correlation analysis was conducted between modular connectivity properties and clinical parameters. Modules interaction analyses showed that the DCM group had significantly greater inter-module connections than the HCs group (DMN-FPN: t = 2.38, p = 0.02); inversely, the DCM group had significantly lower intra-module connections than the HCs group (SMN: t = − 2.13, p = 0.036). Compared to HCs, DCM patients exhibited higher nodal topological properties in the default-mode network and frontal–parietal network. In contrast, DCM patients exhibited lower nodal topological properties in the sensorimotor network. The Japanese Orthopedic Association (JOA) score was positively correlated with inter-module connections (r = 0.330, FDR p = 0.029) but not correlated with intra-module connections. This study reported alterations in modular connections and nodal centralities in DCM patients. Decreased nodal topological properties and intra-modular connection in the sensory-motor regions may indicate sensory-motor dysfunction. Additionally, increased nodal topological properties and inter-modular connection in the default mode network and frontal-parietal network may serve as a compensatory mechanism for sensory-motor dysfunction in DCM patients. This could provide an implicative neural basis to better understand alterations in brain networks and the patterns of changes in brain plasticity in DCM patients.

Degenerative cervical myelopathy (DCM) is the leading cause of spinal cord dysfunction in adults worldwide.DCM encompasses various acquired (age-related) and congenital pathologies related to degeneration of the cervical spinal column, including hypertrophy and/or calcification of the ligaments, intervertebral discs and osseous tissues, these pathologies narrow the spinal canal, leading to chronic spinal cord compression and disability 1,2 .Injuries caused by spinal cord compression include reversible and irreversible injuries, with the majority of patients demonstrating recovery of neurologic function postoperatively, whereas irreversible injuries caused by longer-term compression have unsatisfactory postoperative outcomes 3 .
The plasticity of the mammalian sensorimotor system is the basis of motor learning ability and the ability to relearn after injury; corticomotor and somatosensory sensations show spontaneous reorganization after spinal cord injury; this plasticity contributes to the recovery of motor and sensory functions and provides targets for therapeutic interventions 4 .At the core of sensorimotor integration after injury is the strengthening of circuits

MRI data acquisition
All participants performed 3.0 T MRI (Siemens Trio Tim, Erlangen, Germany) scan with a 4-channel cervical coil and an 8-channel head coil.Before the scan, subjects were asked to stay awake without intense mental activity, close their eyes, and lie comfortably on the examination bed.Sagittal and axial images of the brain and cervical spinal cord were collected, including conventional T1WI, T2WI, and fluid attenuated inversion recovery T2WI.Conventional MR scan was performed to diagnose and exclude brain disorders (such as tumor, cerebral infarction, hemorrhage, encephalomalacia foci) and cervical spinal cord disease (such as multiple sclerosis, amyotrophic lateral sclerosis, and intramedullary tumors).( 1

Data preprocessing
The R-fMRI data preprocessing was performed using the GRETNA toolbox (http:// www.nitrc.org/ proje cts/ gretna/) based on SPM12 (http:// www.fil.ion.ucl.ac.uk/ spm/ softw are/ spm12/).The preprocessing procedure included (1) discarding the first 10 time points of the images for MR signal equilibrium, (2) slice timing correction, (3) head motion correction, (4) space normalization, registering functional data to the corresponding structural T1-weighted image and aligning the T1 images to Montreal Neurological Institute (MNI) space with resampling to a voxel size of 3 × 3 × 3 mm 3 , and (5) nuisance covariate regressions were performed (including 24 motion parameters, white matter, and cerebrospinal fluid signals.(6) The resulting images were further temporally band-pass filtered (0.01-0.1 Hz) to reduce the effects of low-frequency drift and high-frequency physiological noise.Two patients and two controls were excluded based on the criterion of a displacement > 3 mm or an angular rotation > 3 degrees in any direction.

Network construction
To obtain defined nodes, we applied a functional template as proposed in a previous study 17 .This functional template can parcellate the brain into 160 functionally segregated regions of interest (ROIs) that cover most of the cerebral cortex and cerebellum.The set of ROIs (3 mm diameter spheres) were generated using the peak coordinates derived from a series of meta-analyses of fMRI activation studies 17 .We chose this functional template for defining the functional network nodes given that it has been broadly applied to examine whole-brain functional connectivity during resting-state 18 .Average time courses from each ROI were extracted and pair-wise Pearson correlation coefficients were computed between these ROIs.There are no available gold-standard criteria to determine a precise sparsity threshold.Therefore, we explored graph correlation matrices with a wide range of sparsity thresholds from 0.04 to 0.4, with an interval of 0.01.This resulted in 37 sparse connectivity matrices.The minimum and maximum values of the sparsity threshold were established to ensure that threshold networks were estimable for small-worldness (σ) scalar and that σ was greater than 1.1.
According to the study of Dosenbach et al. 17 , the 160 ROIs have been assigned into six functional modules, corresponding to the default-mode network (DMN), frontal-parietal network (FPN), cingulo-opercular network (CONN), sensorimotor network (SMN), visual network, and the cerebellum (Fig. 1).After module decomposition, to find modules that were representative of both groups, we extracted nodes that belonged to the same module in both groups as representative of that module for both groups.Next, we computed the inter-/intra-module connections for each module and each subject.We analyzed the six modules interaction area under the curve (AUC) over the whole range of the sparsity threshold.We calculated the nodal topological properties included degree centrality (Dc), nodal efficiency (Ne), betweenness centrality (Bc) and participant coefficient (PC) of all nodes that were part of exceptionally connected modules.These network properties have previously been defined by Zhang et al. 19 .Moreover, the area under the curve (AUC) of all network metrics was constructed over the whole range of the sparsity threshold.The AUC provides a summarized scalar for the topological characterization of brain networks, independent of a single threshold selection, sensitive in detecting topological alterations of brain disorders, and unravel between-group differences in network organization.www.nature.com/scientificreports/

Statistical analysis
Differences in demographic and clinical variables Statistical analyses were conducted using SPSS 25.0 (IBM).The difference in categorical variables between groups was tested and compared using a chi-squared test, while that between continuous variables was evaluated using nonparametric permutation tests.

Group comparisons based on modular connections and nodal topological metrics
To evaluate differences of intra-and inter-modular connectivity properties and nodal topological properties between DCM and HCs groups, we used a two-sample t-test (Bonferroni p value < 0.05).If between group significant differences were observed in any modules, then partial correlation analysis was conducted to assess the relationships between modularity, the Japanese Orthopedic Association (JOA) score, and Neck Disability Index (NDI) score in the DCM group with age and gender as covariates The significance levels were set at p < 0.05 (FDR corrected).

Demographics and clinical characteristics
There was no significant difference in sex (p = 0.610) and age (p = 0.782) between DCM patients and HCs.DCM patients had a mean symptom duration of 8.72 ± 4.54 months and mean JOA score of 11.22 ± 2.36 (Table 1).

Changes in characteristics over module
Modules interaction analyses showed that the DCM group had significantly greater inter-module connections than the HCs group (DMN-FPN: t = 2.38, p = 0.020); On the contrary, the DCM group had significantly less intra-module connections than the HCs group (SMN: t = − 2.13, p = 0.036) (Fig. 2).www.nature.com/scientificreports/

Relationships between modular measures and clinical variables
The Japanese Orthopedic Association (JOA) score was positively correlated with inter-module connections (r = 0.330, FDR p = 0.029) but not correlated with intra-module connections.and there was also no correlation between module connections and NDI score (Fig. 4).

Discussion
In this current study, modules interaction analyses showed that, in the DCM group, intra-module connections decreased significantly within the SMN, but there was no correlation with the JOA score.In contrast, intermodule connections between the DMN and the frontal-parietal network increased significantly, which was positively correlated with the JOA score.Then we calculated the nodal topological properties of all nodes that were part of exceptionally connected modules.The nodal topological properties were higher in DCM patients than in controls in the right superior frontal gyrus, left post cingulate cortex, left occipital, right ventrolateral prefrontal cortex, left anterior cingulate cortex and left dorsolateral prefrontal cortex.DCM patients had lower nodal topological properties than in controls in the sensorimotor network.One finding of this study is that intra-module connections and nodal topological properties were lower within the SMN network in the DCM group.Previous studies demonstrated alterations in activation volume 20,21 and functional connectivity 22 in the sensorimotor cortex (SMC), an important brain network in patients with DCM.Advanced study also reported metabolite 23 and cerebral blood flow 24 changes in both the motor and sensory cortices.We have found that the modularity within the SMN is reduced in DCM patients, indicating that functional brain networks become less specific.Functionally, this pattern in DCM patients may be related to a drop in sensorimotor function.Following the reduced modularity, the local efficiency within the SMN network also decreased, which were mainly located in the were mainly located in the vFC, SMA, bilateral mid insula and left temporal.We report for the first time changes in the ventral frontal cortex, but we still don't know its role in DCM.There is increasing evidence indicating that SMA is crucial for gait initiation prior to voluntary movement 25 and Ryan et al. 26 also indicated that the volume of activation of the SMA decreased in DCM patients.Galhardoni et al. 27 have found that insula neurons can modulate different dimensions of pain.A review suggests that temporal lobe linked to memory, emotion and executive functions 28 .We speculate that patient motor dysfunction may be related to the decline of its executive capacity.In summary, changes in the sensorimotor network may underlie the production of clinical symptoms.
On the contrary, topological properties of nodes within the DMN and FPN, as well as inter-network connections between the DMN and FPN, were increased in the DCM group compared to the healthy control group.The significance of the DMN is role in cognition and it's been linked to a variety of mental illnesses 29 .Further, recent studies have intermittently identified symptoms beyond the cord, including depression, anxiety and cognitive deficits 30,31 .The DMN also provides a spatial framework for multiple large-scale networks 32 .Our results also showed increased nodal topological properties in the FPN including right vlPFC, left ACC and left dlPFC.The lateral prefrontal cortex(LPFC) showed significant capability in coding of visual information, behavioral decision and widespread information exchange [33][34][35] , reflecting multiple aspects and levels [36][37][38] of executive control.And the ACC is a key region for pain processing and can modulate the neuronal activity for neuropathic pain 39 .Increased node strength indicates enhanced physiological functioning, suggesting a compensatory role for sensory-motor impairment in DCM.
Additionally, we found higher JOA score was associated with greater inter-modular connection.It indicates that milder clinical symptom of DCM may be due to compensatory effects of brain reorganization processes.The increased inter-module connections represented strengthening connections between the DMN and FPN and information transmission.Specifically, the compression of the cervical cord led to decreased modularity and increased modular integrity in cerebrum.
Several limitations in the current study are noteworthy.First, changes in patients after decompression surgery were not evaluated and longitudinal studies could be conducted in the future to better establish a cause and effect relationship between the clinical/imaging variables analyzed and surgical outcome.Additionally, we found the connection of the default-mode network was altered in DCM patients compared to controls.As this network plays an important role in cognition, more scales assessing cognitive function should have been included in our analysis.Finally, our study did not classify the severity of spinal cord compression, which may be associated with brain plasticity.
In conclusion, decreased nodal topological properties and intra-modular connection in the sensory-motor regions may represent the potential physiological basis for sensory-motor impairment.Furthermore, increased nodal topological properties and inter-modular connection in the DMN and the FPN of DCM patients could be a compensatory for sensory-motor dysfunction in DCM.This could provide an implicative neural basis to better understand alterations in brain networks and the patterns of changes in brain plasticity in DCM patients.

Figure 3 .
Figure 3. Between-group comparisons of nodal topological properties of all nodes that were part of exceptionally connected modules.Red circles represent DMN, yellow circles represent PFN, blue circles represent SMN.Bigger and smaller circles represent higher and lower nodal topological properties, respectively, as observed in DCM patients compared with healthy controls.superior frontal gyrus (sup frontal); ventrolateral prefrontal cortex (vlPFC); anterior cingulate cortex (ACC); dorsolateral prefrontal cortex (dlPFC); ventral frontal cortex (vFC); supplementary motor cortex (SMA); R, right; L, left.

Figure 4 .
Figure 4. Correlation analysis of modular connection and clinical variables in DCM patients.The JOA score positively correlated with inter-module connections between DMN and FPN.

Table 1 .
DemographicCompared to HCs, DCM patients exhibited higher nodal topological properties in the right superior frontal gyrus, left post cingulate gyrus, left occipital, right ventrolateral prefrontal cortex (vlPFC), left anterior cingulate cortex (ACC) and left dorsolateral prefrontal cortex (dlPFC).In contrast, compared to the HCs group, DCM patients exhibited lower nodal topological properties in the ventral frontal cortex (vFC), SMA, bilateral mid insula and left temporal (Table2and Fig.3).